Barriers to invest in NFTs: An innovation resistance theory perspective
Bibliographic record
Abstract
Investment in non-fungible tokens (NFTs) has decreased dramatically over the past two years, despite the financial value and potential importance of NFTs for the future of the economy and the current decentralized marketplaces. This study investigated the barriers influencing customers' resistance to investing in NFTs using the innovation resistance theory (IRT) components such as usage barriers, value barriers, risk barriers, tradition barriers, and image barriers. The data was gathered from 375 investors via an online questionnaire. To assess and evaluate the suggested model and its hypotheses, responses were investigated using a partial least square structural equation modeling approach (PLS-SEM). The findings indicate that the five resistance-related barriers are all substantial deterrents to investing in NFTs. The usage barrier was the most significant barrier, whereas the value barrier was the least significant. The study's findings have far-reaching implications for academics, NFTs’ marketplaces, policymakers, and investors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".